Open lakehouse on Apache Iceberg

I build open data lakehouses on Apache Iceberg so your analytics gets the behavior of a full warehouse right on top of object storage — without expensive proprietary engines and without locking your data to one vendor. I design table layout, stand up a catalog, and turn a pile of raw Parquet files into managed versioned tables that any engine can read. The goal is simple: give you ACID transactions, reliable schema evolution, and predictable performance while your data stays in open formats you fully own.

I start from your current state — raw files, a mess of Hive tables, or expensive storage you want to break free from. I model tables, choose partitioning and sorting to match how you really write queries, and enable hidden partitioning so analysts don't wrestle with partition columns manually. I set up schema evolution so adding and renaming columns doesn't rewrite history, bring up time travel and snapshot rollback — a bad load is fixed in one line, not turned into an incident. On the engine side I integrate Spark for batch and maintenance, Trino for interactive SQL, and Flink for streaming — all on the same tables through a shared catalog, with documentation and team training.

— Iceberg table design, partitioning, and sorting
— Catalog, migration from Hive or raw Parquet
— Schema evolution, time travel, and snapshot rollback
— Spark, Trino, and Flink integration on shared tables